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The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy.

Background Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques. Methods In this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network GB that learns to map an image X from a source domain A to a target domain B, i.e. GB:XA→XB. In addition, a discriminator network DB is trained to distinguish whether an image from domain B is real or generated. The same process is applied to another generator-discriminator pair (GA,DA), for the inverse mapping GA:XB→XA. Cycle consistency ensures that a generated image is close to its original when being mapped backwards (GA(GB(XA))≈XA and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma data set for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. In addition, we apply stain normalization on pathological lymph node data from our institute and test the gain from normalization on a ResNet classifier pre-trained on the Camelyon16 data set. Results Qualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%. Conclusions CycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch. The data set supporting the solutions is available at 10.11588/data/8LKEZF.

Preclinical research of myelodysplastic syndromes (MDSs) is hampered by a lack of feasible disease models. Previously, we have established a robust patient-derived xenograft (PDX) model for MDS. Here we demonstrate for the first time that this model is applicable as a preclinical platform to address pending clinical questions by interrogating the efficacy and safety of the thrombopoietin receptor agonist eltrombopag. Our preclinical study included n = 49 xenografts generated from n = 9 MDS patient samples. Substance efficacy was evidenced by FACS-based human platelet quantification and clonal bone marrow evolution was reconstructed by serial whole-exome sequencing of the PDX samples. In contrast to clinical trials in humans, this experimental setup allowed vehicle- and replicate-controlled analyses on a patient–individual level deciphering substance-specific effects from natural disease progression. We found that eltrombopag effectively stimulated thrombopoiesis in MDS PDX without adversely affecting the patients’ clonal composition. In conclusion, our MDS PDX model is a useful tool for testing new therapeutic concepts in MDS preceding clinical trials.

Renal cell carcinoma (RCC) is the deadliest primary genitourinary malignancy typically associated with asymptomatic initial presentation and poorly predictable survival. Next to established risk factors, tumor microenvironment may alter metastatic capacity and immune landscape. Due to their high concentrations, sulfoglycolipids (sulfatides) were among the first well-described antigens in RCC that are associated with worse prognosis. As sulfatide detection in routine diagnostics is not possible, we aimed to test the prognostic value of its protein counterpart, sulfatide-producing enzyme Gal3ST1. We performed retrospective long-term follow up analysis of Gal3ST1 expression as prognostic risk factor in a representative RCC patient cohort. We observed differentially regulated Gal3ST1 expression in all RCC types, being significantly more associated with clear cell RCC than to chromophobe RCC (p = 0.001). Surprisingly, in contrast to published observations from in vitro models, we could not confirm an association between Gal3ST1 expression and a malignant clinical behaviour of the RCC. In our cohort, Gal3ST1 did not significantly influence progression-free survival (Hazard Ratio (HR): 1.7 95% CI (0.6–4.9), p = 0.327). Particularly after adjusting for histology, T-stage, N-status and M-status at baseline, we observed no independent prognostic effect (HR = 1.0 95% CI (0.3–3.3), p = 0.96). The analysis of Gal3ST1 mRNA expression in a TCGA dataset supported the results of our cohort. Thus, Gal3ST1 might help to differentiate between chromophobe RCC and other frequent RCC entities but—despite previously published data from cell culture models—does not qualify as a prognostic marker for RCC. Further investigation of regulatory mechanisms of sulfatide metabolism in human RCC microenvironment is necessary to understand the role of this quantitatively prominent glycosphingolipid in RCC progression.

The thymus prevents autoimmune diseases through mechanisms that operate in the cortex and medulla, comprising positive and negative selection and the generation of regulatory T-cells (Tregs). Egress from the thymus through the perivascular space (PVS) to the blood is another possible checkpoint, as shown by some autoimmune/immunodeficiency syndromes. In polygenic autoimmune diseases, subtle thymic dysfunctions may compound genetic, hormonal and environmental cues. Here, we cover (a) tolerance-inducing cell types, whether thymic epithelial or tuft cells, or dendritic, B- or thymic myoid cells; (b) tolerance-inducing mechanisms and their failure in relation to thymic anatomic compartments, and with special emphasis on human monogenic and polygenic autoimmune diseases and the related thymic pathologies, if known; (c) polymorphisms and mutations of tolerance-related genes with an impact on positive selection (e.g. the gene encoding the thymoproteasome-specific subunit, PSMB11), promiscuous gene expression (e.g. AIRE, PRKDC, FEZF2, CHD4), Treg development (e.g. SATB1, FOXP3), T-cell migration (e.g. TAGAP) and egress from the thymus (e.g. MTS1, CORO1A); (d) myasthenia gravis as the prototypic outcome of an inflamed or disordered neoplastic ‘sick thymus’.

Rhabdomyosarcomas (RMS) are rare and often lethal diseases. It is assumed that the tumor microenvironment (TME) of RMS exerts an immunosuppressive function, but there is currently no systematic analysis of the immune cells infiltrating sarcoma tissue. Focusing on two common types of RMS (alveolar [RMA] and embryonal [RME]), we performed a comprehensive immunohistochemical analysis of tumor-infiltrating immune cells in the TME. We performed a qualitative estimation of infiltrating immune cells in the tumor microenvironment by an experienced pathologist as well as a quantitative digital pathology analysis. We found that (1) manual and automatic quantification of tumor-infiltrating immune cells were consistent; (2) RME tumors showed a higher degree of immune cell infiltration than RMA tumors but (3) the number of tumor infiltrating lymphocytes was low compared to other solid tumor types; (4) microvascular density correlated with immune cell infiltration and (5) CD163 positive macrophages as well as CD54 positive microvessels were more often detected in RME than in RMA and correlated with patient overall and event free survival. Our systematic analysis provides a comprehensive view of the immune landscape of RMS which needs to be taken into account for developing immunotherapies for this rare type of cancer.

Background: Perioperative cisplatin-based chemotherapy (CBC) can improve the outcome of patients with muscle-invasive bladder cancer (MIBC), but it is still to be defined which patients benefit. Mutations in DNA damage response genes (DDRG) can predict the response to CBC. The value of DDRG expression as a marker of CBC treatment effect remains unclear. Material and methods: RNA expression of the nine key DDRG (BCL2, BRCA1, BRCA2, ERCC2, ERCC6, FOXM1, RAD50, RAD51, and RAD52) was assessed by qRT-PCR in a cohort of 61 MICB patients (median age 66 y, 48 males, 13 females) who underwent radical cystectomy in a tertiary care center. The results were validated in the The Cancer Genome Atlas (TCGA) cohort of MIBC (n = 383). Gene expression was correlated with disease-free survival (DFS) and overall survival (OS). Subgroup analyses were performed in patients who received adjuvant cisplatin-based chemotherapy (ACBC) (Mannheim n = 20 and TCGA n = 75). Results: Low expression of RAD52 was associated with low DFS in both the Mannheim and the TCGA cohorts (Mannheim: p = 0.039, TCGA: p = 0.017). This was especially apparent in subgroups treated with ACBC (Mannheim: p = 0.0059, TCGA: p = 0.012). Several other genes showed an influence on DFS in the Mannheim cohort (BRCA2, ERCC2, FOXM1) where low expression was associated with poor DFS (p &lt, 0.05 for all). This finding was not fully supported by the data in the TCGA cohort, where high expression of FOXM1 and BRCA2 correlated with poor DFS. Conclusion: Low expression of RAD52 correlated with decreased DFS in the Mannheim and the TCGA cohort. This effect was especially pronounced in the subset of patients who received ACBC, making it a promising indicator for response to ACBC on the level of gene expression.

Background Growing evidence supports the pivotal role of long non-coding RNAs (lncRNAs) in the regulation of cancer development and progression. Their expression patterns and biological function in muscle invasive bladder cancer (MIBC) remain elusive. Methods Transcript levels of lncRNA miR-31 host gene (MIR31HG) and its splice variants were measured in our MIBC cohort (n = 102) by qRT-PCR, and validated in silico by the TCGA cohort (n = 370). Kaplan-Meier and multiple Cox regression analysis were conducted to evaluate the survival significance of MIR31HG and its splice variants. Functional experiments were performed to examine the proliferation and migration abilities of MIR31HG and its splice variants by knockdown approaches. Results In this study, a decreased expression of MIR31HG was found in bladder cancer cells and tissues, except in the basal subtype. Survival analysis showed that high expression of MIR31HG was associated with poor overall survival (OS) and disease-free survival (DFS) in patients with MIBC of basal subtype. Two splice variants of MIR31HG lacking exon 1 (MIR31HGΔE1) and exon 3 (MIR31HGΔE3) were identified to have specific expression patterns in different molecular subtypes of our MIBC cohort. MIR31HGΔE3 was highly expressed in basal subtype tumors. A high expression of MIR31HGΔE1 and MIR31HGΔE3 was associated with worse OS and DFS in our cohort. In vitro experiments revealed that knockdown of MIR31HG inhibits cell proliferation, colony formation, and migration in bladder cancer. Cell proliferation and migration assays after knockdown of splice variants of MIR31HG showed corresponding roles for the full-length transcript. Conclusions Our study demonstrates that MIR31HG and its splice variants could serve as biomarkers for the classification and prognosis prediction of patients with MIBC.

Current outcome prediction markers for localized prostate cancer (PCa) are insufficient. The impact of the lipid-modifying Sphingomyelin Phosphodiesterase Acid Like 3B (SMPDL3B) in PCa is unknown. Two cohorts of patients with PCa who underwent radical prostatectomy (n = 40, n = 56) and benign prostate hyperplasia (BPH) controls (n = 8, n = 11) were profiled for SMPDL3B expression with qRT-PCR. Publicly available PCa cohorts (Memorial Sloane Kettering Cancer Centre (MSKCC, n = 131, n = 29 controls) and The Cancer Genome Atlas (TCGA, n = 497, n = 53 controls)) served for validation. SMPDL3B&rsquo, s impact on proliferation and migration was analyzed in PC3 cells by siRNA knockdown. In both cohorts, a Gleason score and T stage independent significant overexpression of SMPDL3B was seen in PCa compared to BPH (p &lt, 0.001 each). A lower expression of SMPDL3B was associated with a shorter overall survival (OS) (p = 0.005) in long term follow-up. A SMPDL3B overexpression in PCa tissue was confirmed in the validation cohorts (p &lt, 0.001 each). In the TCGA patients with low SMPDL3B expression, biochemical recurrence-free survival (p = 0.011) and progression-free interval (p &lt, 0.001) were shorter. Knockdown of SMPDL3B impaired PC3 cell migration but not proliferation (p = 0.0081). In summary, SMPLD3B is highly overexpressed in PCa tissue, is inversely associated with localized PCa prognosis, and impairs PCa cell migration.

Invasive pulmonary aspergillosis (IPA) is one of the major complications in immunocompromised patients. The mainstay of diagnostic imaging is non-enhanced chest-computed-tomography (CT), for which various non-specific signs for IPA have been described. However, contrast-enhanced CT pulmonary angiography (CTPA) has shown promising results, as the vessel occlusion sign (VOS) seems to be more sensitive and specific for IPA in hematologic patients. The aim of this study was to evaluate the diagnostic accuracy of CTPA in a larger cohort including non-hematologic immunocompromised patients. CTPA studies of 78 consecutive immunocompromised patients with proven/probable IPA were analyzed. 45 immunocompromised patients without IPA served as a control group. Diagnostic performance of CTPA-detected VOS and of radiological signs that do not require contrast-media were analyzed. Of 12 evaluable radiological signs, five were found to be significantly associated with IPA. The VOS showed the highest diagnostic performance with a sensitivity of 0.94, specificity of 0.71 and a diagnostic odds-ratio of 36.8. Regression analysis revealed the two strongest independent radiological predictors for IPA to be the VOS and the halo sign. The VOS is highly suggestive for IPA in immunocompromised patients in general. Thus, contrast-enhanced CTPA superior over non-contrast_enhanced chest-CT in patients with suspected IPA.

Summary Background The Thymectomy Trial in Non-Thymomatous Myasthenia Gravis Patients Receiving Prednisone (MGTX) showed that thymectomy combined with prednisone was superior to prednisone alone in improving clinical status as measured by the Quantitative Myasthenia Gravis (QMG) score in patients with generalised non-thymomatous myasthenia gravis at 3 years. We investigated the long-term effects of thymectomy up to 5 years on clinical status, medication requirements, and adverse events. Methods We did a rater-blinded 2-year extension study at 36 centres in 15 countries for all patients who completed the randomised controlled MGTX and were willing to participate. MGTX patients were aged 18 to 65 years at enrolment, had generalised non-thymomatous myasthenia gravis of less than 5 years' duration, had acetylcholine receptor antibody titres of 1·00 nmol/L or higher (or concentrations of 0·50–0·99 nmol/L if diagnosis was confirmed by positive edrophonium or abnormal repetitive nerve stimulation, or abnormal single fibre electromyography), had Myasthenia Gravis Foundation of America Clinical Classification Class II–IV disease, and were on optimal anticholinesterase therapy with or without oral corticosteroids. In MGTX, patients were randomly assigned (1:1) to either thymectomy plus prednisone or prednisone alone. All patients in both groups received oral prednisone at doses titrated up to 100 mg on alternate days until they achieved minimal manifestation status. The primary endpoints of the extension phase were the time-weighted means of the QMG score and alternate-day prednisone dose from month 0 to month 60. Analyses were by intention to treat. The trial is registered with ClinicalTrials.gov, number NCT00294658. It is closed to new participants, with follow-up completed. Findings Of the 111 patients who completed the 3-year MGTX, 68 (61%) entered the extension study between Sept 1, 2009, and Aug 26, 2015 (33 in the prednisone alone group and 35 in the prednisone plus thymectomy group). 50 (74%) patients completed the 60-month assessment, 24 in the prednisone alone group and 26 in the prednisone plus thymectomy group. At 5 years, patients in the thymectomy plus prednisone group had significantly lower time-weighted mean QMG scores (5·47 [SD 3·87] vs 9·34 [5·08]; p=0·0007) and mean alternate-day prednisone doses (24 mg [SD 21] vs 48 mg [29]; p=0·0002) than did those in the prednisone alone group. 14 (42%) of 33 patients in the prednisone group, and 12 (34%) of 35 in the thymectomy plus prednisone group, had at least one adverse event by month 60. No treatment-related deaths were reported during the extension phase. Interpretation At 5 years, thymectomy plus prednisone continues to confer benefits in patients with generalised non-thymomatous myasthenia gravis compared with prednisone alone. Although caution is appropriate when generalising our findings because of the small sample size of our study, they nevertheless provide further support for the benefits of thymectomy in patients with generalised non-thymomatous myasthenia gravis. Funding National Institutes of Health, National Institute of Neurological Disorders and Stroke.

Background For virtually every patient with colorectal cancer (CRC), hematoxylin–eosin (HE)–stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. Methods and findings We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I–IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a “deep stroma score,” which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27–3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I–IV CRC patients from the “Darmkrebs: Chancen der Verhütung durch Screening” (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14–2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5–3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34–2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. Conclusions In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images., Jakob Nikolas Kather and colleagues demonstrate the ability of deep convolutional neural networks to extract prognostically relevant information from routine histopathological images collected from colorectal cancer patients., Author summary Why was this study done? Colorectal cancer (CRC) is a common disease with a variable clinical course, and there is a high clinical need to more accurately predict the outcome of individual patients. For almost every CRC patient, histological slides of tumor tissue are routinely available. Deep learning can be used to extract information from very complex images, and we hypothesized that deep learning can predict clinical outcome directly from histological images of CRC. What did the researchers do and find? We trained a deep neural network to identify different tissue types that are abundant in histological images of CRC, especially nontumorous (“stromal”) tissue types. We showed that this deep neural network can decompose complex tissue into its constituent parts and that the abundance of each of these tissue parts can be aggregated in a prognostic score. In two independent, multicenter patient cohorts, we showed that this score improves survival prediction compared to the Union Internationale Contre le Cancer (UICC) staging system, which is the current state of the art. What do these findings mean? Deep learning is an inexpensive tool to predict the clinical course of CRC patients based on ubiquitously available histological images. Prospective validation studies are needed to firmly establish this biomarker for routine clinical use.

ObjectiveThe aim of the study was to analyze the effect of thymectomy on the proposed disease-specific microRNA (miRNA) biomarkers miR-150-5p and miR-21-5p in patients from the prospective randomized trial of thymectomy in myasthenia gravis (MGTX trial) and to evaluate the longitudinal changes in clinical patterns compared with these miRNA levels.MethodsSerum samples were obtained from 80 patients with MG who were included in the MGTX trial. Thirty-eight patients were randomized to thymectomy plus prednisone treatment, and 42 patients were randomized to prednisone treatment. Serum samples were analyzed for the expression of miR-150-5p and miR-21-5p, with quantitative reverse transcriptase PCR at baseline and at 12, 24, and 36 months after randomization. The inclusion criteria for participation in the MGTX trial were age 18–65 years, generalized myasthenia gravis (Myasthenia Gravis Foundation of America Class II–IV), disease duration of less than 5 years, and seropositivity for acetylcholine receptor antibodies (AChR+).ResultsPatients treated with thymectomy had lower levels of miR-150-5p at 24 months, both compared with baseline values (p = 0.0011) and the prednisone group (p = 0.04). No change in miRNA levels was found in the prednisone group. Levels of miR-21-5p displayed a negative correlation with the prednisone dose within the prednisone-only group (p ≤ 0.001).ConclusionsThymectomy lowers the levels of the proposed biomarker miR-150-5p, which strengthens its position as a potential disease-specific biomarker for AChR+ MG.

The thymectomy specimens from the "thymectomy trial in non-thymomatous myasthenia gravis patients receiving prednisone therapy" (MGTX) underwent rigid and comprehensive work-up, which permits analysis of the spatial distribution of histological and immunohistological features. This analysis revealed strong intra- and inter-case variability. While many histological features (e.g. median percent fat content among different specimens) can easily be correlated with clinical parameters, intra-case spatial variability of histological features has yet defied quantification and statistical evaluation. To overcome this gap in digital pathology, we here propose intra-case entropy of measured histological features in all available slides of a given thymectomy specimen as a quantitative marker of spatial histological heterogeneity. Calculation of entropy led to one value per specimen and histological feature. Through these 'entropy values' the so far neglected degree of spatial histological heterogeneity could be fed into statistical analyses, extending the scope of clinico-pathological correlations.

Background Tumor budding, meaning a detachment of tumor cells at the invasion front of colorectal carcinoma (CRC) into single cells or clusters (

// Jakob Nikolas Kather 1,2 , Alexander Marx 1 , Constantino Carlos Reyes-Aldasoro 3 , Lothar R. Schad 2 , Frank Gerrit Zollner 2 and Cleo-Aron Weis 1 1 Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany 2 Computer Assisted Clinical Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany 3 School of Engineering and Mathematical Sciences, City University London EC1V OHB, United Kingdom Correspondence to: Cleo-Aron Weis, email: // Keywords : tumor angiogenesis, digital pathology, spatial statistics, vessel density Received : March 21, 2015 Accepted : April 08, 2015 Published : June 08, 2015 Abstract Blood vessels in solid tumors are not randomly distributed, but are clustered in angiogenic hotspots. Tumor microvessel density (MVD) within these hotspots correlates with patient survival and is widely used both in diagnostic routine and in clinical trials. Still, these hotspots are usually subjectively defined. There is no unbiased, continuous and explicit representation of tumor vessel distribution in histological whole slide images. This shortcoming distorts angiogenesis measurements and may account for ambiguous results in the literature. In the present study, we describe and evaluate a new method that eliminates this bias and makes angiogenesis quantification more objective and more efficient. Our approach involves automatic slide scanning, automatic image analysis and spatial statistical analysis. By comparing a continuous MVD function of the actual sample to random point patterns, we introduce an objective criterion for hotspot detection: An angiogenic hotspot is defined as a clustering of blood vessels that is very unlikely to occur randomly. We evaluate the proposed method in N =11 images of human colorectal carcinoma samples and compare the results to a blinded human observer. For the first time, we demonstrate the existence of statistically significant hotspots in tumor images and provide a tool to accurately detect these hotspots.

Background: Entropy belongs to the few basic measurable entities in nature. It measures the distance of a closed or open ‘statistical’ system from its present stage to its final stage, and analyzes the probability distribution of the included elements, independently from their meaning. The development can be predicted by use of an ‘ideal transformation’, i.e. additive formula (for example Shannon’s entropy) or by mathematical derivatives such as the more generalized q – entropy, for example Tsallis entropy. Herein, the internal structures of the system are described which include so – called macro – systems. They are created by individual elements or basic micro – systems, and transformed into essentials of tissue – based diagnosis. Entropy and neighborhood: The basic entropy approaches consider a spatially force – independent system, i.e., the calculation of the elements’ probability distribution does not take into account the formation of macro – systems, or the position of the individual elements within the system or between individual elements. The receiver of an informative signal cannot distinguish whether it has been generated in the center or at the boundary of the system. Only the signal’s probability within the information chain and the formation of the chain are informative. However, neighborhood plays an important role in development, maturation, degradation, and dissolution of biological systems. Most cells are generated by cellular division and neighboring cells are more similar in morphology and function than non-neighboring cells. This observation also holds true for ‘higher order’ biological systems such as animal colonies, forests, or even human societies. Thus, a potentially successful approach of estimating the development of a biological system should include neighborhood definitions and considerations. Neighborhood conditioned (MST) entropy and entropy flow: Any definition of a neighborhood condition is based upon the distances between different elements, called objects. The distances can be weighted by additional object features, might be ‘directed’, or might include certain ‘shadow’ conditions (hidden behind another object). The most frequently used algorithm has been introduced by Voronoi in 1902. It can be successfully formulated in graph theory and derived approaches. In microscopic morphology, the construction of weighted minimum spanning trees (MST) is a convincing approach. Living biological systems are open and not closed. They exchange energy, information, and directives for future development with their environment. They have to stabilize their own entropy level against that of their environment. The mandatory entropy exchange or entropy flow from the individual element into its environment or vice versa reflects to the system’s stability and impact on its environment. Tissue – based diagnosis and entropy: Tissue – based diagnosis includes all technical procedures to ascertain a ‘medical diagnosis’, such as microscopic, electron microscopic investigations, gene analysis, proteomics, syntactic structure analysis, liquid biopsies, etc. Herein, the transformation and applicability of the entropy approach are described and discussed., Diagnostic Pathology, Vol 3 No 1 (2017): 2017

Background: Besides clinical stage and Gleason score, risk-stratification of prostate cancer in the pretherapeutic setting mainly relies on the serum PSA level. Yet, this is associated with many uncertainties. With regard to therapy decision-making, additional markers are needed to allow an exact risk prediction. Eukaryotic translation elongation factor 1 alpha 2 (EEF1A2) was previously suggested as driver of tumor progression and potential biomarker. In the present study its functional and prognostic relevance in prostate cancer was investigated. Methods: EEF1A2 expression was analyzed in two cohorts of patients (n = 40 and n = 59) with localized PCa. Additionally data from two large expression dataset (MSKCC, Cell, 2010 with n = 131 localized, n = 19 metastatic PCa and TCGA provisional data, n = 499) of PCa patients were reanalyzed. The expression of EEF1A2 was correlated with histopathology features and biochemical recurrence (BCR). To evaluate the influence of EEF1A2 on proliferation and migration of metastatic PC3 cells, siRNA interference was used. Statistical significance was tested with t-test, Mann-Whitney-test, Pearson correlation and log-rank test. Results: qRT-PCR revealed EEF1A2 to be significantly overexpressed in PCa tissue, with an increase according to tumor stage in one cohort (p = 0.0443). In silico analyses in the MSKCC cohort confirmed the overexpression of EEF1A2 in localized PCa with high Gleason score (p = 0.0142) and in metastatic lesions (p = 0.0038). Patients with EEF1A2 overexpression had a significantly shorter BCR-free survival (p = 0.0028). EEF1A2 expression was not correlated with serum PSA levels. Similar results were seen in the TCGA cohort, where EEF1A2 overexpression only occurred in tumors with Gleason 7 or higher. Patients with elevated EEF1A2 expression had a significantly shorter BCR-free survival (p = 0.043). EEF1A2 knockdown significantly impaired the migration, but not the proliferation of metastatic PC3 cells. Conclusion: The overexpression of EEF1A2 is a frequent event in localized PCa and is associated with histopathology features and a shorter biochemical recurrence-free survival. Due to its independence from serum PSA levels, EEF1A2 could serve as valuable biomarker in risk-stratification of localized PCa.

Blood vessels in cancer Intra-tumoral blood vessels are of supreme importance for tumor growth, metastasis and therapy. Yet, little is known about spatial distribution patterns of these vessels. Most experimental or theoretical tumor models implicitly assume that blood vessels are equally abundant in different parts of the tumor, which has far-reaching implications for chemotherapy and tumor metabolism. In contrast, based on histological observations, we hypothesized that blood vessels follow specific spatial distribution patterns in colorectal cancer tissue. We developed and applied a novel computational approach to identify spatial patterns of angiogenesis in histological whole-slide images of human colorectal cancer. A characteristic spatial pattern of blood vessels in colorectal cancer In 33 of 34 (97%) colorectal cancer primary tumors blood vessels were significantly aggregated in a sharply limited belt-like zone at the interface of tumor tissue to the intestinal lumen. In contrast, in 11 of 11 (100%) colorectal cancer liver metastases, a similar hypervascularized zone could be found at the boundary to surrounding liver tissue. Also, in an independent validation cohort, we found this vascular belt zone: 22 of 23 (96%) samples of primary tumors and 15 of 16 (94%) samples of liver metastases exhibited the above-mentioned spatial distribution. Summary and implications We report consistent spatial patterns of tumor vascularization that may have far-reaching implications for models of drug distribution, tumor metabolism and tumor growth: luminal hypervascularization in colorectal cancer primary tumors is a previously overlooked feature of cancer tissue. In colorectal cancer liver metastases, we describe a corresponding pattern at the invasive margin. These findings add another puzzle piece to the complex concept of tumor heterogeneity.

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.